The most comprehensive cloud based services for text understanding and the simplest to use for developers with no AI expertise.
LUIS interacts with the user in the natural language to complete a task
Examples of applications that can be make with the help of Luis are:
- AI ChatBots
- Speech Enabled Desktop Application
- Social Media Apps
Improves recognition through machine learning and teaching
Extract intents and entities from utterances
Basic Terminology in LUIS
An intent represents a task or action the user wants to perform.
For eg: A Travel app have following intents
By default a None intent is made which is a fallback intent
There are some pre built intents that LUIS provide by default
Entities are data you want to pull from the utterance, such as names, dates, product names, or any significant group of words. An utterance can include many entities or none at all.
These are some pre built entities present in LUIS
List entity is used to add the list of entities that can be found in an intent
For eg: Create an entity CourseList
Add values in the List
It will be automatically identified in your Intents
It is the input from the user that needs to be analysed
To train LUIS to extract intents and entities from them, it’s important to capture a variety of different example utterances for each intent.
You can test your model after training it with multiple intents, entities and utterances
You will get the topic score as well as sentiment and entities related to the sentence
You can publish your model on staging or on production
You can turn on or off Sentiment Analysis for the Report as per as your requirement and publish the model
Once you publish the model you will get a Query Endpoint which can be appended at the end with the query and you will get a json result corresponding to that Query and the result includes topScore,intents,entities,sentiment analysis(optional) etc.
Now let’s fire the following Query in the API
https://testnandita.cognitiveservices.azure.com/luis/prediction/v3.0/apps/6373702d-1552-4aad-8785-cb4a6d0c47da/slots/staging/predict?subscription-key=f848502a91a946dca396665b0aa3ec3b&verbose=true&show-all-intents=true&log=true&query=What time is science lecture
The JSON Response for the result is
You can clearly see that the response consists of topIntent and respective intents with there scores ,entities and sentiment analysis with it’s score
For more information about LUIS you can refer to this docs